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Research on Optimizing and Systemati...

Jiang, Hang,

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Research on Optimizing and Systematizing Internet Bidding Advertising for Local Lifestyle Services = = 关于本地生活服务互联网竞价广告优化与系统化研究 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Research on Optimizing and Systematizing Internet Bidding Advertising for Local Lifestyle Services =/ Hang Jiang.
Reminder of title:	关于本地生活服务互联网竞价广告优化与系统化研究 /
Author:	Jiang, Hang,
Description:	1 electronic resource (86 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Home economics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997176
ISBN:	9798314877616

Research on Optimizing and Systematizing Internet Bidding Advertising for Local Lifestyle Services = = 关于本地生活服务互联网竞价广告优化与系统化研究 /
Jiang, Hang,

Research on Optimizing and Systematizing Internet Bidding Advertising for Local Lifestyle Services =关于本地生活服务互联网竞价广告优化与系统化研究 /Hang Jiang. - 1 electronic resource (86 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

Driven by the digital economy, the local service industry is accelerating digital transformation, with internet bidding ads becoming core connectors between merchants and consumers through precise targeting and measurable outcomes. This study focuses on ad optimization and system architecture, proposing strategic frameworks to overcome industry bottlenecks.Analyzing mobile internet's 98.3% penetration and China's 3,530 billion yuan O2O market (2019-2023), we identify three challenges in bidding ads: performance volatility, rising costs, and experience fragmentation. Using 120 million ad records from Meituan and Douyin, our multi-factor CTR/CVR models reveal creative quality impacts 37.2% of click-through rates, while landing page experience determines 51.8% of conversions. Experiments show: emotional creatives boost CTR by 42.3%, 10% geo-targeting precision reduces CAC by ¥6.8, and reinforcement learning increases ROI 29.7%. To address algorithm homogeneity and data silos, we propose a Dual-Cycle Optimization Model: inner-cycle optimizes creatives via LSTM and dynamic bidding; outer-cycle establishes full-chain systems through data platforms. Implemented at Hema Fresh, this reduced CAC by 18.4% and increased in-store conversion 26.9%. A four-dimensional framework includes: 1) knowledge graph-driven creative automation; 2) federated learning cutting budget waste by 32.7%; 3) cross-channel monitoring; 4) adaptive anti-fraud systems.Case studies show 23.6% lower ad spend with 41.8% order growth for F&B chains, and CAC reduction from ¥58 to ¥32 for home-service platforms. This research pioneers service-scenario parameterization algorithms and closed-loop optimization systems, paving the way for 5G/AR immersive ads and AI-driven neuromarketing.

English

ISBN: 9798314877616Subjects--Topical Terms:

551902
Home economics.
Subjects--Index Terms:

Ad attribution

Research on Optimizing and Systematizing Internet Bidding Advertising for Local Lifestyle Services = = 关于本地生活服务互联网竞价广告优化与系统化研究 /
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500 $a Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
500 $a Advisors: Huang, Xiaochuan; Zhu, Ning Committee members: Chen, Xin.
502 $b D.B.A. $c Arizona State University $d 2025.
520 $a Driven by the digital economy, the local service industry is accelerating digital transformation, with internet bidding ads becoming core connectors between merchants and consumers through precise targeting and measurable outcomes. This study focuses on ad optimization and system architecture, proposing strategic frameworks to overcome industry bottlenecks.Analyzing mobile internet's 98.3% penetration and China's 3,530 billion yuan O2O market (2019-2023), we identify three challenges in bidding ads: performance volatility, rising costs, and experience fragmentation. Using 120 million ad records from Meituan and Douyin, our multi-factor CTR/CVR models reveal creative quality impacts 37.2% of click-through rates, while landing page experience determines 51.8% of conversions. Experiments show: emotional creatives boost CTR by 42.3%, 10% geo-targeting precision reduces CAC by ¥6.8, and reinforcement learning increases ROI 29.7%. To address algorithm homogeneity and data silos, we propose a Dual-Cycle Optimization Model: inner-cycle optimizes creatives via LSTM and dynamic bidding; outer-cycle establishes full-chain systems through data platforms. Implemented at Hema Fresh, this reduced CAC by 18.4% and increased in-store conversion 26.9%. A four-dimensional framework includes: 1) knowledge graph-driven creative automation; 2) federated learning cutting budget waste by 32.7%; 3) cross-channel monitoring; 4) adaptive anti-fraud systems.Case studies show 23.6% lower ad spend with 41.8% order growth for F&B chains, and CAC reduction from ¥58 to ¥32 for home-service platforms. This research pioneers service-scenario parameterization algorithms and closed-loop optimization systems, paving the way for 5G/AR immersive ads and AI-driven neuromarketing.
520 $a 数字经济时代驱动下，本地生活服务行业加速数字化转型，互联网竞价广告凭借精准定位与效果量化优势，成为商户与消费者的核心连接纽带。本文聚焦该领域广告优化与系统构建，通过多维分析提出策略体系，助力行业突破发展瓶颈。研究基于移动互联网98.3%渗透率、O2O市场35.3万亿元规模的产业背景，揭示竞价广告面临效果波动、成本攀升、体验断层三重挑战。通过抓取美团、抖音等平台2019-2023年1.2亿条广告数据，构建CTR/CVR多因素模型，发现素材质量对点击率影响达37.2%，着陆页体验对转化率决定作用高达51.8%。实验验证：情感化场景素材提升点击率42.3%，地域定向精度每增10%可降获客成本6.8元，强化学习策略使ROI提升29.7%。针对算法同质化、数据孤岛、体验断层等结构性矛盾，创新提出"双循环优化模型"：内循环依托LSTM创意生成与动态出价算法实现要素调优；外循环通过数据中台与智能投放构建全链路系统。该模型在盒马鲜生应用中实现获客成本降18.4%、到店转化率升26.9%。同步建立"四维优化体系"：1）知识图谱驱动智能创意库，自动化适配文案素材；2）联邦学习支撑多平台联合竞价，减少32.7%预算浪费；3）全渠道监测矩阵覆盖曝光至履约全流程；4）自适应风控模块实时拦截异常流量。实践验证显示，连锁餐饮品牌应用后广告支出降23.6%且订单增41.8%，家政平台获客成本从58元降至32元。研究突破传统广告评估框架，首创服务场景参数化算法体系，构建"诊断-优化-监控"闭环系统。未来将探索5G/AR沉浸式广告与神经营销结合，持续推动行业智能化升级。.
546 $a English
590 $a School code: 0010
650 4 $a Home economics. $3 551902
650 4 $a Web studies. $3 2122754
653 $a Ad attribution
653 $a Bidding algorithm optimization
653 $a Digital advertising
653 $a Intelligent marketing systems
653 $a Local services
653 $a O2O transformation
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690 $a 0646
690 $a 0338
690 $a 0800
690 $a 0386
710 2 $a Arizona State University. $b Business Administration. $e degree granting institution. $3 3771863
720 1 $a Huang, Xiaochuan $e degree supervisor.
720 1 $a Zhu, Ning $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-11B.
790 $a 0010
791 $a D.B.A.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997176